Deep learning has attracted intense interest in Prognostics and Health Management (PHM), because of its enormous representing power, automated feature learning capability and best-in-class performance in solving complex problems. This paper surveys recent advancements in PHM methodologies using deep learning with the aim of identifying research gaps and suggesting further improvements. After a brief introduction to several deep learning models, we review and analyze applications of fault detection, diagnosis and prognosis using deep learning. The survey validates the universal applicability of deep learning to various types of input in PHM, including vibration, imagery, time-series and structured data. It also reveals that deep learning provides a one-fits-all framework for the primary PHM subfields: fault detection uses either reconstruction error or stacks a binary classifier on top of the network to detect anomalies; fault diagnosis typically adds a soft-max layer to perform multi-class classification; prognosis adds a continuous regression layer to predict remaining useful life. The general framework suggests the possibility of transfer learning across PHM applications. The survey reveals some common properties and identifies the research gaps in each PHM subfield. It concludes by summarizing some major challenges and potential opportunities in the domain.

Caerulomycins (CAEs) and collismycins (COLs), which mainly differ in sulfur decoration, are two groups of structurally similar natural products containing a 2,2'-bipyridine (2,2'-BP) core, derivatives of which have been widely used in chemistry. The biosynthetic pathways of CAEs and COLs remain elusive. In this work, cloning of the CAE biosynthetic gene cluster allowed us to mine a highly conserved gene cluster encoding COL biosynthesis in a Streptomyces strain that was previously unknown as a 2,2'-BP producer. In vitro and in vivo investigations into the biosynthesis revealed that CAEs and COLs share a common paradigm featuring an atypical hybrid polyketide synthase/nonribosomal peptide synthetase system that programs the 2,2'-BP formation. This likely involves an unusual intramolecular cyclization/rearrangement sequence, and a difference in processing of the sulfhydryl group derived from the same precursor cysteine drives the biosynthetic route toward CAEs or COLs.

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